# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch.onnx
import timm
import sys

def pth2onnx(input_file,output_file):
    model = timm.create_model('regnety_016', pretrained=False)
    checkpoint = torch.load(input_file, map_location=None)
    model.load_state_dict(checkpoint)
    
    model.eval()
    input_names = ["image"]
    output_names = ["class"]
    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}}
    dummy_input = torch.randn(1, 3, 224, 224)
    torch.onnx.export(model,
     dummy_input, 
     output_file,
      input_names = input_names,
       dynamic_axes = dynamic_axes,
        output_names = output_names,
     opset_version=11, verbose=True)



if __name__ == '__main__':
    pth2onnx(sys.argv[1],sys.argv[2])